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Value Iteration-Based adaptive Fuzzy Backstepping Optimal Control of Modular Robot Manipulators via Integral Reinforcement Learning

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2024年第4期26卷 1347-1363页

作者： Dong, Bo Jiang, Hucheng Cui, Yiming Zhu, Xinye An, Tianjiao Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

An adaptive fuzzy backstepping optimal control method is developed for modular robot manipulators (MRMs) via value iteration (VI). This paper adopts joint torque feedback (JTF) technique to construct subsystem dynamics model, and the state space description is deduced. According to fusion function which contains the error in joint angular velocity and position, the cost function is established. The integral reinforcement learning (IRL) is integrated into the VI algorithm, which solves the optimal tracking control issue without system drift dynamics. For purpose of improving the control effect, the optimal tracking control issue of manipulator can be reconsidered as the optimal compensation issue which adopting the local dynamics information. Then the uncertainty in the model can be compensated by an adaptive fuzzy backstepping compensation controller which is constructed by fuzzy logic system (FLS) and backstepping control method. The optimal compensation control strategy is adopted to deal with the interconnected dynamic coupling (IDC), which contains global information about each joint. Based on the VI algorithm and adaptive dynamic programming (ADP) method, an effective solution of Hamiltonian-Jacobi-Bellman (HJB) equation is presented. According to Lyapunov theorem, the trajectory tracking error is uniformly ultimately bounded (UUB) by using the adaptive fuzzy backstepping optimal control method. Finally, the effectiveness of the proposed method is verified by experiments.

关键词： Integral reinforcement learning adaptive dynamic programming Value iteration Modular robot manipulators Fuzzy logic system

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Static/dynamic event-triggered learning control for constrained nonlinear systems

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NONLINEAR dynamicS 2024年第16期112卷 14159-14174页

作者： Hu, Lingzhi Wang, Ding Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

This paper designs two novel event-triggered control (ETC) schemes based on the critic learning technique for constrained discrete-time nonlinear systems. First, starting from the stability of the constrained system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the static event-triggered mechanism, so as to establish the dynamic ETC method, which further improves the resource utilization rate and possesses the anti-interference ability. Moreover, a speedy value iteration architecture is designed to obtain an initially admissible optimal control policy, which can ensure the normal execution of the designed ETC methods. Finally, two experimental examples are provided to illustrate the effectiveness and superiority of the developed schemes.

关键词： adaptive critic control adaptive dynamic programming Constrained nonlinear systems Speedy value iteration Static and dynamic event-triggered control

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adaptive Optimal Control for Input-constrained Nonlinear Discrete-time System With Stage Cost Learning

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第8期22卷 2444-2454页

作者： Wang, Jianfeng Wang, Yan Ji, Zhicheng Jiangnan Univ Engn Res Ctr Internet Things Technol Applicat Minist Educ Wuxi 214122 Peoples R China

This paper investigates the problem of input-constrained optimal control for nonaffine nonlinear discrete-time systems in the presence of an inaccurate model. To address this problem, a bounded function is used to convert the input-constrained optimal control problem into an unconstrained counterpart. Additionally, an optimal stage cost function is introduced to quantify the discrepancy between the inaccurate model and the true system dynamics. Subsequently, a policy iteration based stage cost learning (PISCL) algorithm is proposed to obtain the optimal stage cost function and the convergence of the algorithm is proved. The proposed approach provides a new framework for addressing input-constrained control problems of nonaffine nonlinear systems with an inaccurate model, bridging the gap between model-based and data-based approximate dynamic programming techniques. Numerical experiments validate the effectiveness of the PISCL algorithm in obtaining the constrained optimal control policies for nonaffine nonlinear discrete-time systems without precise system models.

关键词： adaptive dynamic programming input-constrained optimal control stage cost function

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adaptive critic design for safety-optimal FTC of unknown nonlinear systems with constrained

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ISA TRANSACTIONS 2024年 155卷 309-318页

作者： Zhang, Dehua Wang, Yuchen Meng, Lei Yan, Jiayuan Qin, Chunbin Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

Safe fault tolerant control is one of the key technologies to improve the reliability of dynamic complex nonlinear systems with limited inputs, which is hard to solve and definitely a great challenge to tackle. Thus the paper presents a novel safety-optimal FTC (Fault Tolerant Control) approach for a category of completely unknown nonlinear systems incorporating actuator fault and asymmetric constrained-input, which can guarantee the system's operation within a safe range while showcasing optimal performance. Firstly, a CBF (Control Barrier Function) is incorporated into the cost function to penalize unsafe behaviors, and then we translate the intractable safety-optimal FTC problem into a differential ZSG (Zero-Sum Game) problem by defining the control input and the actuator fault as two opposing sides. Secondly, a neural-network-based identifier is employed to reconstruct system dynamics using system data, and the resolution of handling asymmetric constrained-input with the introduced non-quadratic cost function is achieved through the design of an adaptive critic scheme, aiming to reduce computational expenses accordingly. Finally, through the theoretical stability analysis, it is demonstrated that all signals in the closed-loop system are consistently UUB (Uniformly Ultimately Bounded). Furthermore, the proposed method's effectiveness is also verified in the simulation experiments conducted on a model of a single-link robotic arm system with actuator failure. The result shows that the algorithm can fulfill the safety-optimal demand of fault tolerant control in fault system with asymmetric constrained-input.

关键词： Fault tolerant control Safety-optimal Asymmetric constrained-input adaptive dynamic programming Nonlinear systems

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Fractional-Order Systems Optimal Control via Actor-Critic Reinforcement Learning and Its Validation for Chaotic MFET

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年 22卷 1173-1182页

作者： Li, Dongdong Dong, Jiuxiang Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang 110819 Peoples R China

Since the existence of fractional order dynamics, it is difficult to obtain an optimality equation to solve for fractional-order optimal control. In this paper, a fractional Hamilton-Jacobi-Bellman (HJB) equation based on error derivative is proposed, and a corresponding online learning algorithm is designed. The scheme can handle the optimal tracking problem for the 0 < alpha <= 1 order nonlinear systems. Since the traditional quadratic cost function is unbounded at infinite time and the optimal control derived from the discounted cost function fails to stabilize the system asymptotically, a cost function based on the error derivative is proposed, which can avoid these problems, and the system is not restricted to be zero equilibrium. Then, the fractional HJB equation is derived by constructing an auxiliary signal without directly using the chain rule of differentiation. The optimality, stability and convergence of its solution are proved, and actor-critic neural networks (NNs) are established to perform the RL algorithm. Finally, the algorithm is applied to a chaotic magnetic-field electromechanical transducer (MFET) system to verify the effectiveness and advantages.

关键词： Fractional-order systems optimal control reinforcement learning (RL) neural networks adaptive dynamic programming

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Improved value iteration for nonlinear tracking control with accelerated learning

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第6期34卷 4112-4131页

作者： Wang, Ding Wang, Yuan Ha, Mingming Ren, Jin Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Computat Intelligence & Intelligent Syst Beijing Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

In this article, an adaptive critic scheme with a novel performance index function is developed to solve the tracking control problem, which eliminates the tracking error and possesses the adjustable convergence rate in the offline learning process. Under some conditions, the convergence and monotonicity of the accelerated value function sequence can be guaranteed. Combining the advantages of the adjustable and general value iteration schemes, an integrated algorithm is proposed with a fast guaranteed convergence, which involves two stages, namely the acceleration stage and the convergence stage. Moreover, an effective approach is given to adaptively determine the acceleration interval. With this operation, the fast convergence of the new value iteration scheme can be fully utilized. Finally, compared with the general value iteration, the numerical results are presented to verify the fast convergence and the tracking performance of the developed adaptive critic design.

关键词： adaptive critic designs adaptive dynamic programming fast convergence nonlinear tracking control value iteration

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Finite-time optimal control for uncertain strict-feedback nonlinear systems with input saturation and output constraints

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2024年第2期38卷 580-603页

作者： Xia, Xiaonan Li, Chun Zhang, Tianping Fang, Yu Yangzhou Univ Coll Informat Engn Dept Automat Yangzhou 225127 Peoples R China

In this paper, a finite-time optimal control (FTOC) strategy is proposed for output constrained uncertain nonlinear systems with input saturation. The controller adopts a feed-forward and optimal feedback control structure. The second-order command filter and the auxiliary error compensation system are designed in the feed-forward controller, which can eliminate the influence of filtering error on system performance while avoiding the singularity problem of finite-time control. A new critic weight updating law is proposed in the design of the optimal feedback controller, in which a neural network is utilized to approximate the relevant cost function. The control scheme can ensure that all signals in the optimize system are semi-global practical finite-time stable (SGPFS), and the cost function is also minimized. The effectiveness of the algorithm is validated through simulation examples.

关键词： adaptive dynamic programming finite-time control input saturation optimal control output constraints

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adaptive Fuzzy Control for T-S Fuzzy Fractional Order Nonautonomous Systems Based on Q-learning

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第2期32卷 388-397页

作者： Sun, Jiayue Yan, Yuqing Yu, Shuhang Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

In this article, fractional-order nonautonomous system (FONAS) with the input delay and nonlinear terms are considered and investigated using adaptive fuzzy control method based on Q-learning. With the novel estimation model, the defined predictions for the error system determines the weights of the fuzzy logic system (FLS). On this basis, an error derivative-based cost function is introduced, which not only deals with the classic problem that quadratic term cost function is unbounded in infinite time, but also resolves the challenge that the exponential discount factor cost function fails to stabilize asymptotically. For the unmeasurable part of the state, the designed fuzzy observer eliminates the restriction on the gain parameters. Furthermore, based on the measured information and the actor-critic architecture of the online training FLSs, the improved adaptive fault-tolerant control (FTC) input approximate the optimal control. Utilizing a fractional-order Lyapunov method, the stability of FONAS with actuator faults is discussed, and a sufficient criterion for stability is obtained, which is easier to perform with convex optimization tools. Finally, numerical simulations are shown to display the effectiveness of the optimal adaptive fuzzy FTC strategy.

关键词： adaptive dynamic programming adaptive fuzzy control fractional-order system input delay

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adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第10期46卷 2001-2012页

作者： Vu, Nga Thi-Thuy Nguyen, Hieu Xuan Bui, Manh Quang Hanoi Univ Sci & Technol Sch Elect & Elect Hanoi 11615 Vietnam

The operation of the three-phase standalone inverter is affected by many factors, such as heavy changes in load, unbalanced loads, nonlinear loads, system uncertainties and external disturbances. These are critical weaknesses for the control system of the three-phase inverter to reach robust optimal performance. This paper proposes an adaptive optimal sliding mode control (AOSMC) scheme for a three-phase nonlinear uncertain inverter. This AOSMC strategy solves the problems of nonlinear optimization by adaptive dynamic programming, one of the techniques of reinforcement learning, and overcomes the uncertainties and disturbance effects by a disturbance observer-based sliding mode controller. This algorithm uses only one neural network to approximate the critic;therefore, the burden of computation is significantly reduced. Both the weight matrix of the critic network and the disturbance observer are asymptotically stable. The overall system is guaranteed to be ultimately uniformly bounded stable via Lyapunov stable theory. The simulation is conducted to validate the insensitivity of the proposed AOSMC algorithm to working conditions. Also, the competitive results are presented to demonstrate the improvement of the proposed AOSMC scheme in comparison to some other existing controllers.

关键词： adaptive dynamic programming disturbance observer sliding mode control three-phase voltage source inverter uncertainty

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Semi-global output regulation of continuous-time linear systems subject to input saturation via output feedback: A model-free method

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2024年第12期38卷 3756-3770页

作者： Fu, Zhuofan Zhao, Zhiyun East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China

This article considers the output regulation problem of continuous-time linear systems subject to input saturation in the scenario that the system dynamics are unknown. Based on the low-gain technique and the output feedback technique, we construct an output feedback control law to solve the output regulation problem with input saturation. Since the system dynamics are unknown, we estimate the feedback and feedforward terms in the control law with a two-step data-driven method. An output feedback adaptive dynamic programming algorithm is proposed to estimate the feedback term. An online updating algorithm based on output tracking error is proposed to estimate the feedforward term directly without solving the output regulation equations. Both algorithms don't rely on the measurement of the internal states of the exosystem and the original system. We show that the input saturation caused by the iterative feedforward gain matrix in the online updating algorithm doesn't affect the convergence of the algorithm. With these two data-driven algorithms, the control law doesn't rely on the dynamics anymore. The low-gain control law obtained by the model-free method prevents input saturation from occurring and hence solves the output regulation problem. Finally, a numerical example is provided to validate the theoretical results.

关键词： adaptive dynamic programming input saturation model-free output regulation

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